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在视网膜底部照片中检测糖尿病视网膜病变的深度学习算法的开发和验证
Varun Gulshan1, Lily Peng1, Marc Coram1
1Google Inc, Mountain View, California.
JAMA
|November 30, 2016
概括
一个深度学习算法在视网膜图像中显示出高灵敏度和特异性检测可引用的糖尿病视网膜病变. 需要进一步的研究来证实其临床效用和对患者的结果的影响.
科学领域:
- 眼科 眼科
- 医学成像
- 人工智能
背景情况:
- 糖尿病视网膜病变 (DR) 和糖尿病黄斑 (DME) 是糖尿病患者视力丧失的主要原因.
- 早期发现和治疗对于预防严重的视力损伤至关重要.
研究的目的:
- 开发和验证用于在视网膜底部照片中自动检测DR和DME的深度学习算法.
- 与专家眼科医生分级相比,评估算法的灵敏度和特异性.
主要方法:
- 在超过128,000张视网膜图像上训练了一个深度卷积神经网络.
- 该算法在两个独立数据集 (EyePACS-1和Messidor-2) 上进行了验证.
- 使用灵敏度,特异性和接收器操作曲线下的面积来评估性能.
主要成果:
- 该算法在检测可转移的糖尿病视网膜病变 (RDR) 中取得了高性能.
- 接收器操作曲线下的面积为0.991 (EyePACS-1) 和0.990 (Messidor-2).
- 在高特异性操作点,敏感度分别为90.3%和87.0%,特异性分别为98.1%和98.5%.
结论:
- 深度学习算法可以实现高灵敏度和特异性来检测可引用的糖尿病视网膜病变.
- 需要进一步研究以确定该算法的临床可行性和对患者护理的影响.
- 这些发现表明,人工智能有可能增强糖尿病眼病的眼科评估.
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